cleaner data, higher adoption, better leads

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Cleaner Data, Higher Adoption, Better Leads Marketing Professionals Jorge Villamariona: salesforce.com Jennifer Taylor: SunGard Data Systems Jon Miller: Marketo James Cleveland: salesforce.com

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How accurate is your business data? Do you have duplicate records? Keeping data clean and relevant for your users is no small task. Join us for a session dedicated to data cleansing and de-duplication and learn techniques for better managing your data. We'll focus on Excel and file import, Web-to-lead, de-duplication, creation of validated fields, lead scoring with formula fields, and using Jigsaw to create automated data cleansing. Take home all the methods, tools, and best practices you need!

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Page 1: Cleaner Data, Higher Adoption, Better Leads

Cleaner Data, Higher Adoption, Better Leads

Marketing Professionals

Jorge Villamariona: salesforce.comJennifer Taylor: SunGard Data SystemsJon Miller: MarketoJames Cleveland: salesforce.com

Page 2: Cleaner Data, Higher Adoption, Better Leads

Safe HarborSafe harbor statement under the Private Securities Litigation Reform Act of 1995: This presentation may contain forward-looking statements that involve risks, uncertainties, and assumptions. If any such uncertainties materialize or if any of the assumptions proves incorrect, the results of salesforce.com, inc. could differ materially from the results expressed or implied by the forward-looking statements we make. All statements other than statements of historical fact could be deemed forward-looking, including any projections of subscriber growth, earnings, revenues, or other financial items and any statements regarding strategies or plans of management for future operations, statements of belief, any statements concerning new, planned, or upgraded services or technology developments and customer contracts or use of our services.

The risks and uncertainties referred to above include – but are not limited to – risks associated with developing and delivering new functionality for our service, our new business model, our past operating losses, possible fluctuations in our operating results and rate of growth, interruptions or delays in our Web hosting, breach of our security measures, the outcome of intellectual property and other litigation, risks associated with possible mergers and acquisitions, the immature market in which we operate, our relatively limited operating history, our ability to expand, retain, and motivate our employees and manage our growth, new releases of our service and successful customer deployment, our limited history reselling non-salesforce.com products, and utilization and selling to larger enterprise customers. Further information on potential factors that could affect the financial results of salesforce.com, inc. is included in our annual report on Form 10-K for the most recent fiscal year ended January 31, 2010. This documents and others are available on the SEC Filings section of the Investor Information section of our Web site.

Any unreleased services or features referenced in this or other press releases or public statements are not currently available and may not be delivered on time or at all. Customers who purchase our services should make the purchase decisions based upon features that are currently available. Salesforce.com, inc. assumes no obligation and does not intend to update these forward-looking statements.

Page 3: Cleaner Data, Higher Adoption, Better Leads

Jorge Villamariona

Sales Engineer,

salesforce.com

Page 4: Cleaner Data, Higher Adoption, Better Leads

Agenda

Why you need to have a data quality policy and strategy?

Techniques for managing the quality of your leads

How SunGard is keeping their data clean and relevant to increase CRM adoption

Learn how to leverage Jigsaw to obtain new contacts and maintain existing contacts

How Marketo is leveraging Jigsaw to create better leads

Page 5: Cleaner Data, Higher Adoption, Better Leads

Why Data Quality Matters

Understand your customer

Increase customer loyalty

Obtain correct metrics

Make the right decisions

Page 6: Cleaner Data, Higher Adoption, Better Leads

6 Consequences of Bad Data“Analysts rate bad data as one of the top 3 reasons for CRM failure”

1

2

3

4

5

6

Inaccurate report metrics

Users get frustrated. You lose valuable buy-in and adoption

Marketing wastes money and effort pursuing bad prospects

Understanding your “customer” is impossible

IT wastes time sifting through information

Operations has difficulty reconciling data against finance and other back end systems

Page 7: Cleaner Data, Higher Adoption, Better Leads

75% of commercial businesses believe that they are losing as much as 73% of revenue due to poor data quality

Experian - QASU.S. Business Losing Revenue Through

Poorly Managed Customer Data

Importance of Data QualityThe Cost of Bad Data

75% ofrespondents

41% ofrespondents

Poor data quality costs U.S. businesses more than $600 billion annually

Data Warehousing Institute ”“

Page 8: Cleaner Data, Higher Adoption, Better Leads

How Do We Manage Data Quality?

Data Culture

Analyze

Establish a mindset that data quality is everyone’s responsibility

Analyze and understand your data

Set goals and map your path to get there

Implement data management processes to improve your data quality

Leverage integrations and automation to drive data quality

Deploy mechanisms to protect and track data quality against goals

Standardize, Enrich & Clean

Integrate & Automate

Maintain

Plan

Page 9: Cleaner Data, Higher Adoption, Better Leads

Instilling a Culture of Data Quality

EducateAnything goes, adoption before data integrity

EducateAnything goes, adoption before data integrity

UnderstandRecognize usage trends,Adapt standards to reality

UnderstandRecognize usage trends,Adapt standards to reality

StandardizeTrain to common « best practices »

StandardizeTrain to common « best practices »

Rewards / DisincentivesReinforce best practices,with a carrot AND a stick

Rewards / DisincentivesReinforce best practices,with a carrot AND a stick

IntegrateBuild tools to help multidepartment tasks / processes

IntegrateBuild tools to help multidepartment tasks / processes

AutomateMake everybody’s job easier,and make the company more efficient

AutomateMake everybody’s job easier,and make the company more efficient

1 2 3

456

Page 10: Cleaner Data, Higher Adoption, Better Leads

Analyze: Data Profiling

Data Sources Data weaknesses Mapping & Usage

Page 11: Cleaner Data, Higher Adoption, Better Leads

Data Quality Analysis Example: Phone Numbers

Not valid

Not standardized

Not complete

Page 12: Cleaner Data, Higher Adoption, Better Leads

Plan: Data Quality Management Strategy

Create your data quality plan Identify and prioritize goals

Define reports and dashboards

Find sponsors and owners

Establish budget

Select tools (i.e. for de-duplication)

Commit resources

Create communication plan

Page 13: Cleaner Data, Higher Adoption, Better Leads

Standardize, Clean, and Enrich

Names Load to Sandbox

Find & Replace

1 2 5

StandardizeStandardize CleanseCleanse ValidateValidate

US, U.S, U.S.A -> USA Acme-Widgets-453

Hot HighCold Low

Data Transformation

acme incorp.-> Acme Inc

Naming Conventions

Addresses

Mergers, acquisitions, spin-offs

Company Name & Address

Enrich/Integrate/Automate

Enrich/Integrate/Automate

Acme Inc HQAcme UK

Hierarchy Data

Demographics

4

Postal Standards

Identify, Match & Score

3

De-dupeDe-dupe

J. Smith, John Smith – 80%

Re-parent Child Records

Account: Division, Opportunity, Contact

Merge

J. Smith, John Smith -> John Smith

Archiving & Filtering

Validate & Modify

Load to Production

Page 14: Cleaner Data, Higher Adoption, Better Leads

Standardize

Accounts names: Inc vs. Incorp., INC, incorporated; Ltd vs LTD, Limited

Opportunity names: i.e. Name – Product: “Acme – 250 Tschotchkes”

Country/State: use validation to standardize TX vs Texas, USA vs. U.S.

Postal Code: use validation rules for proper format in US/CAN: xxxxx-xxxx

Contact info: use pick lists for roles, titles, department: Marketing vd. Mktg

Look for useful validation rules in Help & Training!

Page 15: Cleaner Data, Higher Adoption, Better Leads

Cleanse your Data

Correct inaccuracies and inconsistencies

Find and replace bad or missing data

Remove or merge duplicates

Leverage all users to fix data (it’s their data)

Archive irrelevant and old data

Leverage automated routines/tools

Routinely reconcile Salesforce data against other data

points/systems

Page 16: Cleaner Data, Higher Adoption, Better Leads

Cleanse your Data

Prioritize your data control process Fix high visibility/usage information first (duplicates, addresses,

emails)

Fix business specific information next (opportunity types,

stages etc)

Remove duplicate fields (don’t repeat account info on contact)

Remove irrelevant fields

Page 17: Cleaner Data, Higher Adoption, Better Leads

Enrich: Data Augmentation

Add missing information from 3rd party sources

Understand what data would provide additional value

Add internally available account intelligence

Page 18: Cleaner Data, Higher Adoption, Better Leads

Integrate

Understand your masters

Avoid stale and bad information

Make information read only

Create a 360 view

Page 19: Cleaner Data, Higher Adoption, Better Leads

4 Resources to Help Automate

Workflow Jigsaw

Force.comIntegration points

Page 20: Cleaner Data, Higher Adoption, Better Leads

list not all encompassing

Data Management ApplicationsForce.com Appexchange app considerations

Sample Tools

Composite Apps• Enterprise Mash-ups• Rich user interface

Application Integration• Real-time integration•Multi-step integration• Human workflow

Data Integration• Data migration• Data replication• Bulk Data Transfers

Data Cleansing• Data de-duplication• Data assessment

4

Page 21: Cleaner Data, Higher Adoption, Better Leads

Tools for Managing Data Quality

Web-to-X

Leverage tools to prevent duplicates before passing to Salesforce real-time

Import data from various file sources

Validation Rules

Use Validation Rules and Workflow

Data Quality Analytics

Use reports and dashboards to measure data quality

Analyze and cleanse data

Excel Connector Data Loader

Page 22: Cleaner Data, Higher Adoption, Better Leads

Web to X

• Prevent duplicates leads when using W2L

• Visit our AppExchange for more information

Page 23: Cleaner Data, Higher Adoption, Better Leads

Validation Rules

• Enforce your standards before you save the record

Page 24: Cleaner Data, Higher Adoption, Better Leads

Excel Connector

• Analyze• Cleanse• Enforce your standards

Page 25: Cleaner Data, Higher Adoption, Better Leads

Data Loader

• Extract• Analyze• Cleanse• Import

Page 26: Cleaner Data, Higher Adoption, Better Leads

Reports & Dashboards

Use reports & dashboards to measure quality

Page 28: Cleaner Data, Higher Adoption, Better Leads

Jennifer Taylor

Marketing Operations Manager,

SunGard

Page 29: Cleaner Data, Higher Adoption, Better Leads

All About SunGard

After 25 years of developing software and managing the systems that help financial services institutions run their business, SunGard is still a leader. Our prestigious track record of industry awards is a reflection of our deep understanding of the specialized, mission-critical business processes that lie at the heart of the banking, securities and investments, and insurance industries.  From the beginning, we have applied our expertise to better serve our customers.

• INDUSTRY: Software & IT Services• EMPLOYEES: 16,000 • GEOGRAPHY: Global• # USERS: 2000+• PRODUCT(S) USED: SFA, Marketing, Service & Support, 5

downloaded AppExchange applications

Com

Page 30: Cleaner Data, Higher Adoption, Better Leads

Overview

What is a lead?

How we manage lead imports through cases and

DemandTools

How we use SPOT to get a holistic view of “genuine”

duplicate leads

Sales tools to research and validate leads

Page 31: Cleaner Data, Higher Adoption, Better Leads

What is a Lead to SunGard?

Leads are an extension of the sales funnel – “above the

funnel” information

Leads are potential opportunities (with contact

information)

Just as a contact can be associated to multiple

opportunities, the same contact information can be on

multiple leads

Leads are only de-duped within a segment/product line

Page 32: Cleaner Data, Higher Adoption, Better Leads

What is a Lead to SunGard?

Page 33: Cleaner Data, Higher Adoption, Better Leads

How SunGard Manages Lead Imports

All lead import/de-dupe requests are submitted via internal

salesforce.com case with an attached import spreadsheet

using a provided template

Lead imports offered as a centralized serviced (not viewed as

a user limitation)

The standard turnaround time for a lead import/de-dupe is

24 – 72 hours

We use Demand Tool’s People Import to de-dupe on

segment/product line, email address, and last name when

importing

Page 34: Cleaner Data, Higher Adoption, Better Leads

How SunGard Manages Lead Imports

Page 35: Cleaner Data, Higher Adoption, Better Leads

How SunGard Manages Lead Imports

Page 36: Cleaner Data, Higher Adoption, Better Leads

SPOT – Single Point of Truth

SPOT joins all leads/contacts with the same email

address for a holistic view of:– Subscription preferences

– Campaign activity

– Email results

SPOT helps eliminate concerns of “genuine” duplicates

providing a holistic view

Page 37: Cleaner Data, Higher Adoption, Better Leads

SPOT – Single Point of Truth

Page 38: Cleaner Data, Higher Adoption, Better Leads

“LinkedIn” from Lead or Contact

Page 39: Cleaner Data, Higher Adoption, Better Leads

Updates from Jigsaw from a Lead or Contact

Page 40: Cleaner Data, Higher Adoption, Better Leads

VP Marketing, Marketo

Author Modern B2B Marketing

Jon Miller

Page 41: Cleaner Data, Higher Adoption, Better Leads

About Marketo

Revenue Performance Management solutions (marketing automation, sales insight, ROI analytics) that significantly increase marketing and sales success for more than 800 mid-sized and enterprise worldwide

• Salesforce• Jigsaw for Salesforce• Salesforce Community• Marketo Lead Management / Marketing Automation• Marketo Sales Insight• Marketo Revenue Cycle Analytics

Page 42: Cleaner Data, Higher Adoption, Better Leads

Marketo’s Revenue Cycle

All

Nam

es

Pro

spec

t &

R

ecyc

led

Lea

d

AW

AR

EN

ES

S

En

gag

ed

Opportunity Customer

Sal

esL

ead

MQLSQL

SAL

Marketing SDR Sales

Page 43: Cleaner Data, Higher Adoption, Better Leads

All

Nam

es

Pro

spec

t &

R

ecyc

led

En

gag

ed

Need Quality Information about Prospects

Lea

d

Opportunity

Sal

esL

ead

MQLSQL

SAL

By Industry: 82%By Role: 67%

Doers vs. BuyersJob FunctionBy Company Size: 49%By Geography: 29%

Page 44: Cleaner Data, Higher Adoption, Better Leads

Generating Names and Prospects

Page 45: Cleaner Data, Higher Adoption, Better Leads

Problems With Self-Submitted Data

Page 46: Cleaner Data, Higher Adoption, Better Leads

Short Forms Outperform Long Forms

Short Form (5 fields)

Conversion rate: 13.4%

Cost per: $31.24

Medium Form (7 fields)

Conversion rate: 12.0%

Cost per: $34.94

Long Form (9 fields)

Conversion rate: 10.0%

Cost per: $41.90

Page 47: Cleaner Data, Higher Adoption, Better Leads
Page 48: Cleaner Data, Higher Adoption, Better Leads

Progressive Profiling

Page 49: Cleaner Data, Higher Adoption, Better Leads

Data Augmentation

Real-Time & Batch Import (Weekly)

Page 50: Cleaner Data, Higher Adoption, Better Leads

Jigsaw Results

Matches for 39% of leads and 70% of accounts

Industry, Revenue, and Employee count populated on all

fields

Graveyard leads and contacts moved to unsubscribe

Early but seeing improvements in nurturing quality and

scoring relevance

Page 51: Cleaner Data, Higher Adoption, Better Leads

Manager Corporate Sales, Jigsaw

Jim Cleveland

Page 52: Cleaner Data, Higher Adoption, Better Leads

All Companies Struggle in Some Way with Data

Average Dirty Data Found in Customers Before Jigsaw

90%Incomplete

74%Need Updates

21%Dead

7%Duplicate

Page 53: Cleaner Data, Higher Adoption, Better Leads

The Cloud Shows Us a Better Way to Manage Data

12,568,906 users made…395,177,595 edits to…20,676,516 articles

xxx

Socially Aggregated

The data you need finds you…right away

xx

Real Time

*Source: Shaugatuck Technology 2010 Cloud Business Solutions

2014: 50% of software

deployments in the cloud*

No Software

Stock Data

Weather Data

Restaurant Data

Traffic DataMovie Data

Page 54: Cleaner Data, Higher Adoption, Better Leads

The Crowd is More Precise

70% Direct Dial Numbers

100% Complete Contacts

10xMore Titles

NameTitle Ext. 3415

Director of… Marketing…

Viral Marketing…Enterprise Marketing…

Social Media…Email Marketing…Dept

EmailPhone

Address

Page 55: Cleaner Data, Higher Adoption, Better Leads

Leverage the Cloud with Jigsaw

1.4 million + members200,000 updates/month

Name Phone

Bob Johnson 415-536-6000

Bob Johnson 650-205-1899

Rob Johnson 415-536-6100

Bob C. Johnson

408-209-7070

Bob Johnson 415-536-6000

Rob Johnson 650-205-5555

Bob T. Johnson

650-780-9090

Robert Johnson(415) 536-2283

Improve Productivity with Clean Services

Page 56: Cleaner Data, Higher Adoption, Better Leads

Access Clean Data Within the Sales Cloud

Instant source of 24M+ new leads

Unlimited, free research into 4M+ new accounts

Reps are productive with clean data

for Salesforce

Page 57: Cleaner Data, Higher Adoption, Better Leads

Jigsaw Delivers Across the Complete Plan

Names Load to Sandbox

Find & Replace

1 2 5

StandardizeStandardize CleanseCleanse ValidateValidate

US, U.S, U.S.A -> USA Acme-Widgets-453

Hot HighCold Low

Data Transformation

acme incorp.-> Acme Inc

Naming Conventions

Addresses

Mergers, acquisitions, spin-offs

Company Name & Address

Enrich/Integrate/Automate

Enrich/Integrate/Automate

Acme Inc HQAcme UK

Hierarchy Data

Demographics

4

Postal Standards

Identify, Match & Score

3

De-dupeDe-dupe

J. Smith, John Smith – 80%

Re-parent Child Records

Account: Division, Opportunity, Contact

Merge

J. Smith, John Smith -> John Smith

Archiving & Filtering

Validate & Modify

Load to Production

Page 58: Cleaner Data, Higher Adoption, Better Leads

Jorge Villamariona

Sales Engineer

Jennifer Taylor

Marketing Operations Manager

James Cleveland

Corporate Sales Manager

Question & Answer

Jon Miller Vice President of MarketingTWITTER @jonmiller2

Page 59: Cleaner Data, Higher Adoption, Better Leads

Action Plan

Data Culture

Analyze

Establish a mindset that data quality is everyone’s responsibility

Analyze and understand your data

Set goals and map your path to get there

Implement data management processes to improve your data quality

Leverage integrations and automation to drive data quality

Deploy mechanisms to protect and track data quality against goals

Standardize, Enrich & Clean

Integrate & Automate

Maintain

Plan

Page 60: Cleaner Data, Higher Adoption, Better Leads

Cleaner Data, Higher Adoption, Better Leads

Page 61: Cleaner Data, Higher Adoption, Better Leads

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